SpringerLink
Log in

Facial Recognition Software for Identification of Powered Wheelchair Users

  • Conference paper
  • First Online:
Intelligent Systems and Applications (IntelliSys 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 294))

Included in the following conference series:

  • 1405 Accesses

Abstract

The research presented in this paper investigates the use of facial recognition software as a potential system to identify powered wheelchair users. Facial recognition offers advantages over other biometric systems where wheelchair users have disabilities. Facial recognition systems scan an image or video feed for a face, and compare the detected face to previously detected data. This paper reviews the software development kits and the libraries available for creating such as systems and discusses the technologies chosen to create a prototype facial recognition system. The new prototype system was trained with 262 identification pictures and confidence ratings were produced from the system for video feeds from twelve users. The results from the trials and variance in confidence ratings are discussed with respect to gender, presence of glasses and make up. The results demonstrated the system to be 95% efficient in its ability to identify users.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Sanders, D., Gegov, A.: Using artificial intelligence to share control of a powered-wheelchair between a wheelchair user and an intelligent sensor system. EPSRC Project 2019–2022 (2018)

    Google Scholar 

  2. Haddad, M.J., Sanders, D.A.: Deep Learning architecture to assist with steering a powered wheelchair. IEEE Trans. Neural Syst. Rehabil. Eng. 28, 2987 (2020)

    Article  Google Scholar 

  3. Krops, L.A., Hols, D.H., Folkertsma, N., Dijkstra, P.U., Geertzen, J.H., Dekker, R.: Requirements on a community-based intervention for stimulating physical activity in physically disabled people: a focus group study amongst experts. Disabil. Rehabil. 40(20), 2400–2407 (2018)

    Article  Google Scholar 

  4. Bos, I., Wynia, K., Almansa, J., Drost, G., Kremer, B., Kuks, J.: The prevalence and severity of disease-related disabilities and their impact on quality of life in neuromuscular diseases. Disabil. Rehabil. 41(14), 1676–1681 (2019)

    Article  Google Scholar 

  5. Frank, A.O., De Souza, L.H.: Clinical features of children & adults with a muscular dystrophy using powered indoor/outdoor wheelchairs: disease features, comorbidities and complications of disability. Disabil. Rehabil. 40(9), 1007–1013 (2018)

    Article  Google Scholar 

  6. Sanders, D.A., Langner, M., Tewkesbury, G.E.: Improving wheelchair-driving using a sensor system to control wheelchair-veer and variable-switches as an alternative to digital-switches or joysticks. Ind. Rob. Int. J. 32(2), 157–167 (2010)

    Article  Google Scholar 

  7. Langner, M.: Effort Reduction and Collision Avoidance for Powered Wheelchairs: SCAD Assistive Mobility System (Doctoral dissertation, University of Portsmouth) (2012)

    Google Scholar 

  8. Sanders, D.A., Bausch, N.: Improving steering of a powered wheelchair using an expert system to interpret hand tremor. In: Liu, H., Kubota, N., Zhu, X., Dillmann, R., Zhou, D. (eds.) ICIRA 2015. LNCS (LNAI), vol. 9245, pp. 460–471. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-22876-1_39

    Chapter  Google Scholar 

  9. Sanders, D.A.: Using self-reliance factors to decide how to share control between human powered wheelchair drivers and ultrasonic sensors. IEEE Trans. Neural Syst. Rehabil. Eng. 25(8), 1221–1229 (2016)

    Article  Google Scholar 

  10. Sanders, D.A., Gegov, A., Haddad, M., Ikwan, F., Wiltshire, D., Tan, Y.C.: A rule-based expert system to decide on direction and speed of a powered wheelchair. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2018. AISC, vol. 868, pp. 822–838. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-01054-6_57

    Chapter  Google Scholar 

  11. Sanders, D.A., Haddad, M., Tewkesbury, G.E., Thabet, M., Omoarebun, P., Barker, T.: Simple expert system for intelligent control and HCI for a wheelchair fitted with ultrasonic sensors. In: 2020 IEEE 10th International Conference on Intelligent Systems (IS), pp. 211–216. IEEE, August 2020

    Google Scholar 

  12. Haddad, M., Sanders, D., Gegov, A., Hassan, M., Huang, Y., Al-Mosawi, M.: Combining multiple criteria decision making with vector manipulation to decide on the direction for a powered wheelchair. In: Bi, Y., Bhatia, R., Kapoor, S. (eds.) IntelliSys 2019. AISC, vol. 1037, pp. 680–693. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-29516-5_51

    Chapter  Google Scholar 

  13. Haddad, M., Sanders, D., Langner, M., Ikwan, F., Tewkesbury, G., Gegov, A.: Steering direction for a powered-wheelchair using the analytical hierarchy process. In: Proceedings of the 2020 IEEE 10th International Conference on Intelligent Systems (IS), Varna, Bulgaria, pp. 229–234 (2020)

    Google Scholar 

  14. Haddad, M., et al.: Use of the analytical hierarchy process to determine the steering direction for a powered wheelchair. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2020. AISC, vol. 1252, pp. 617–630. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-55190-2_46

    Chapter  Google Scholar 

  15. Haddad, M.J., Sanders, D.A.: Selecting a best compromise direction for a powered wheelchair using PROMETHEE. IEEE Trans. Neur. Syst. Rehabil. 27(2), 228–235 (2019)

    Article  Google Scholar 

  16. Haddad, M., Sanders, D., Ikwan, F., Thabet, M., Langner, M. and Gegov, A., 2020, August. Intelligent HMI and control for steering a powered wheelchair using a Raspberry Pi microcomputer. In 2020 IEEE 10th International Conference on Intelligent Systems (IS) (pp. 223–228). IEEE.

    Google Scholar 

  17. Haddad, M., et al.: Intelligent control of the steering for a powered wheelchair using a microcomputer. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2020. AISC, vol. 1252, pp. 594–603. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-55190-2_44

    Chapter  Google Scholar 

  18. Tewkesbury, G., Sanders, D., Haddad, M., Bausch, N., Gegov, A., Okonor, O.: Task programming methodology for powered wheelchairs. In: Bi, Y., Bhatia, R., Kapoor, S. (eds.) IntelliSys 2019. AISC, vol. 1037, pp. 711–720. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-29516-5_53

    Chapter  Google Scholar 

  19. Haddad, M., et al.: Intelligent system to analyze data about powered wheelchair drivers. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2020. AISC, vol. 1252, pp. 584–593. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-55190-2_43

    Chapter  Google Scholar 

  20. Haddad, M., Sanders, D., Langner, M., Omoarebun, P., Thabet, M., Gegov, A.: Initial results from using an intelligent system to analyse powered wheelchair users’ data. In: 2020 IEEE 10th International Conference on Intelligent Systems (IS), pp. 241–245. IEEE, August 2020

    Google Scholar 

  21. Sanders, D., Haddad, M., Tewkesbury, G., Bausch, N., Rogers, I., Huang, Y.: Analysis of reaction times and time-delays introduced into an intelligent HCI for a smart wheelchair. In: 2020 IEEE 10th International Conference on Intelligent Systems (IS), pp. 217–222. IEEE, August 2020

    Google Scholar 

  22. Sanders, D., et al.: Introducing time-delays to analyze driver reaction times when using a powered wheelchair. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2020. AISC, vol. 1252, pp. 559–570. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-55190-2_41

    Chapter  Google Scholar 

  23. Haddad, M., et al.: Steering a powered wheelchair using a camera module and image processing algorithms. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) Intelligent Systems and Applications. IntelliSys 2021. Advances in Intelligent Systems and Computing (2021). (Accepted and in Press)

    Google Scholar 

  24. Haddad, M., et al.: Novel approach to steer a powered wheelchair using image processing algorithm and Raspberry Pi. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) Intelligent Systems and Applications. IntelliSys 2021. Advances in Intelligent Systems and Computing (2021). (Accepted and in Press)

    Google Scholar 

  25. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA, p. I (2001). https://doi.org/10.1109/CVPR.2001.990517

  26. Yang, M.-H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 24(1), 34–58 (2002). https://doi.org/10.1109/34.982883

  27. Wang, C.: What’s the Difference Between Haar-Feature Classifiers and Convolutional Neural Networks? Towards Data Science (2018). https://towardsdatascience.com/whats-the-difference-between-haar-feature-classifiers-and-convolutional-neural-networks-ce6828343aeb. Accessed 1 Jan 2021

  28. Sirovich, L., Kirby, M.: Low dimensional procedure for the characterisation of human faces. J. Optical Soc. Am. 4, 519 (1986)

    Article  Google Scholar 

  29. Georgescu, D.: A real-time facial recognition system using eigenfaces. J. Mobile Embedded Distrib. Syst. 3(4), 199. ISSN 2067-4074 (2011)

    Google Scholar 

  30. Williams Pontin, M.: Better Face-Recognition Software. MIT Technology Review (2007). https://www.technologyreview.com/s/407976/better-face-recognition-software/. Accessed 01 Jan 2021

  31. Enriquez, K.: Faster Face Detection using Convolutional Neural Networks & the Viola-Jones Algorithm (2018). https://www.csustan.edu/sites/default/files/groups/University%20Honors%20Program/Journals/01_enriquez.pdf. Accessed 01 Jan 2021

  32. Microsoft: Cognitive Services pricing – Face API (2019). https://azure.microsoft.com/en-gb/pricing/details/cognitive-services/face-api/. Accessed 01 Jan 2021

  33. Amazon: Amazon Rekognition pricing (2019). https://aws.amazon.com/rekognition/pricing/. Accessed 01 Jan 2021

  34. Google: AI for Social Good in Asia Pacific (2018). https://www.blog.google/around-the-globe/google-asia/ai-social-good-asia-pacific/amp/. Accessed 1 Jan-2021

  35. Kairos: KAIROS FACE RECOGNITION PRICING GUIDE (2019). https://www.kairos.com/pricing/. Accessed 01 Jan 2021

  36. Jacobs, H., Ralph, P.: Inside the creepy and impressive startup funded by the Chinese government that is develo** AI that can recognize anyone, anywhere. Business Insider (2018). https://www.businessinsider.com/china-facial-recognition-tech-company-megvii-faceplusplus-2018-5. Accessed 01 Jan 2021

  37. Face++: Face++ Facial Recognition API Prices (2021). https://www.faceplusplus.com/v2/pricing/. Accessed 01 Jan 2021

  38. OpenCV: Open Source Computer Vision Library (2021). https://opencv.org/about.html. Accessed 01 Jan 2021

  39. Accord.NET: Machine Learning Made in a Minute. (2021). http://accord-framework.net/. Accessed 01 Jan 2021

Download references

Author information

Authors and Affiliations

  1. University of Portsmouth, Portsmouth, PO1 3DJ, UK

    Giles Tewkesbury, Samuel Lifton, Malik Haddad, David Sanders & Alex Gegov

Authors
  1. Giles Tewkesbury
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samuel Lifton
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Malik Haddad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David Sanders
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alex Gegov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David Sanders .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tewkesbury, G., Lifton, S., Haddad, M., Sanders, D., Gegov, A. (2022). Facial Recognition Software for Identification of Powered Wheelchair Users. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-82193-7_42

Download citation

Publish with us

Policies and ethics

Search

Navigation